Most organizations don’t suffer from a lack of data, they suffer from a lack of usable data. Teams spend days stitching together extracts for each new question, only to repeat the same work a month later when the question changes slightly.
The idea behind data products is simple: instead of delivering one-off datasets or dashboards, treat carefully designed, well-governed data assets as products in their own right, built for a clear purpose, owned by a team, and safe to reuse across the business.
This article explores what data products are, how they differ from traditional data delivery, and how a platform like Dataoma, which integrates directly with your data warehouse to provide discovery, automatic documentation, and documentation‑driven tests, can help you build and run them in practice.
1. What is a data product?
A data product is a curated, documented, and maintained data asset that solves a specific, repeatable business need. It’s not “whatever table happens to exist today”, it’s something designed with clear consumers, inputs, outputs, and expectations.
Data products can take many forms:
• A standardized “Active Customers” table that powers finance, marketing, and support reports
• A churn-risk score feed used by CRM workflows and customer success teams
• A feature store used by machine learning models across multiple projects
• A set of trusted KPIs exposed to BI tools as a semantic layer
• An API that serves inventory availability to both internal tools and partner applications
What makes these products is not the technology, but the intent: they are designed, owned, and supported like any other product your organization would ship.
2. Why treating data “as a product” changes everything
Most data platforms still operate in a ticket model: someone asks a question, data people assemble an answer, and the result lives in a one-off dashboard or spreadsheet. That model doesn’t scale.
A product mindset introduces several critical shifts:
• From projects to products: data work isn’t “done” at first delivery; it has a lifecycle.
• From hidden heroes to explicit ownership: each product has named stewards accountable for quality and relevance.
• From one-off answers to reusable building blocks: new analyses re-use existing products instead of re-creating logic.
• From hope to guarantees: expectations around freshness, stability, and accuracy are agreed and monitored.
The result is less duplicated work, fewer conflicting numbers, and more confidence in what the warehouse serves to the rest of the business.
3. Essential traits of a good data product
Not every table or dashboard automatically qualifies as a product. Effective data products tend to share a few characteristics.
3.1 Purpose‑driven
• Built around a concrete need or decision, not just “because the data is there”.
• Clear definition of who uses it and what questions it should help answer.
3.2 Discoverable
• Easily found via search, tags, and domains in your catalog.
• Distinguishable from similar assets, with clear “use this, not that” guidance.
3.3 Documented
• Business definitions, metric formulas, assumptions, and caveats are captured in one place.
• Example queries and usage notes help new consumers get started quickly.
3.4 Tested and monitored
• Core expectations (freshness, volume, value ranges, uniqueness) are encoded as automated tests.
• Alerts fire when behavior deviates from what is documented.
3.5 Owned and governed
• A defined owner or team is responsible for evolution, access control, and deprecation.
• Lineage to upstream sources is visible for impact analysis and compliance.
How Dataoma helps: Dataoma connects directly to your data warehouse to automatically profile tables, generate documentation, and convert that documentation into data quality tests. That makes it much easier to meet the “discoverable, documented, tested” bar for every product you create.
4. The lifecycle of a data product
Thinking in terms of a lifecycle keeps data products from becoming “build once and forget” assets. A simple, repeatable flow might look like this:
4.1 Frame the problem
• Start with a business outcome (e.g., reduce churn, improve margin reporting accuracy), not with a specific dataset.
• Identify the primary consumers and decisions they need to make.
4.2 Design the product
• Decide on inputs (source systems), outputs (tables, views, APIs), and success metrics.
• Sketch the schema, core metrics, and grain (e.g., “one row per active subscription per month”).
4.3 Build & integrate
• Implement pipelines and models in your ELT tooling and warehouse.
• Wire the product into BI tools, ML pipelines, or applications as needed.
4.4 Document & profile
• Describe the product in a catalog; attach definitions, owners, and examples.
• Use profiling (like Dataoma’s automatic analysis) to capture actual distributions, null rates, and other behavioral characteristics.
4.5 Publish & onboard
• Announce availability to relevant teams; provide a “getting started” guide.
• Gather early feedback and clarify any confusing edges or constraints.
4.6 Monitor & evolve
• Track usage, test failures, and support questions.
• Retire or merge products that no longer deliver value; version major changes.
5. Data products in the context of modern platforms
Data products don’t exist in a vacuum. They sit on top of your modern data stack and align well with patterns like domain‑oriented architectures and data mesh.
5.1 On top of the warehouse
• Most products are implemented as warehouse models or views, sometimes paired with APIs.
• This makes them easy to secure, scale, and reuse across many tools.
5.2 Owned by domains
• Marketing, finance, operations, and other domains own products that reflect their concepts and responsibilities.
• A central platform team provides shared infrastructure and governance.
5.3 Supported by a discovery & quality layer
• Catalog and observability tools, such as Dataoma, provide the “front door” to these products, along with continuous validation.
Dataoma’s role in the stack: Think of your warehouse as the engine and pipelines as the conveyor belts. Dataoma is the control panel showing what products exist, how they behave, and whether they still match their specification, profiling them, documenting them, and enforcing checks automatically.
6. Designing a data product operating model
To move beyond one or two experiments, you’ll eventually a shared expectations for how data products are created, maintained, and retired.
6.1 Roles
• Data product manager: defines scope, users, and value; prioritizes roadmap.
• Data engineers / analytics engineers: implement pipelines and models in the warehouse.
• Domain experts: validate definitions, metrics, and business relevance.
• Governance & platform teams: set standards for security, documentation, and quality.
6.2 Processes
• Clear intake for new product ideas and change requests.
• Definition of minimal documentation and testing required before publishing.
• Regular reviews of usage, quality signals, and deprecation candidates.
6.3 Tooling
• Warehouse + ELT for implementation.
• Dataoma for discovery, documentation, profiling, and test generation.
• BI and application integration for consumption.
7. Common pitfalls when adopting data products
Like any change in operating model, adopting data products comes with traps. Being aware of them upfront helps you design around them.
7.1 Renaming old assets without changing behavior
• Simply calling a table a “data product” doesn’t make it one. Without ownership, documentation, and tests, it’s just a new label.
7.2 Ignoring the consumer
• Products designed from the warehouse outward rather than from the user backward may be technically elegant but practically unused.
7.3 Over‑engineering too early
• Trying to formalize every dataset as a product on day one leads to overhead and confusion.
• Better: start with a few high‑impact products, then scale patterns that work.
7.4 Letting quality drift
• Without automated checks and visible signals, products gradually diverge from their specifications.
• Dataoma’s documentation‑driven tests help avoid this by aligning what’s written with what the data actually does.
8. How Dataoma accelerates data product success
You can build data products without specialized tooling, but it’s painful. Dataoma is designed to make the hardest parts of the job, discovery, documentation, and quality, significantly easier.
Warehouse‑native profiling
• Connects directly to your warehouse and analyzes tables and columns at scale.
• Surfaces null rates, cardinality, distributions, and anomalies that help you design better products and set realistic expectations.
Automatic documentation
• Generates initial descriptions and summaries based on structure and behavior.
• Centralizes human edits and annotations, so every data product has a living spec close to the data.
Documentation‑powered testing
• Turns documented assumptions, such as “this column is unique” or “values are between 0 and 100”, into executable tests that run against the warehouse.
• Alerts you when reality no longer matches what’s specified, before downstream users notice.
Unified discovery layer
• Gives analysts, engineers, and business users one place to search for data products, understand how to use them, and see their current health.
Conclusion: from data piles to data products
Moving to a data product mindset won’t happen overnight, but the direction is clear. Organizations that treat their most important datasets as designed, owned, and monitored products are better equipped to support AI, experimentation, and everyday decision‑making.
Cloud warehouses and modern ELT tools give you the technical foundation. A product operating model provides the process. A platform like Dataoma gives you the layer of discovery, documentation, and testing that keeps those products usable and trustworthy over time.
If your warehouse is full of tables but short on clear, reusable assets that people actually trust, data products, and the right tooling around them are the next logical step.
Turn your warehouse tables into real data products with Dataoma →